Automated analysis of digital oximetry in the diagnosis of obstructive sleep apnoea

• Published in: Thorax Vol. 55; no. 4; pp. 302 - 307
• Main Authors: Vázquez, Juan-Carlos, Tsai, Willis H, Flemons, W Ward, Masuda, Akira, Brant, Rollin, Hajduk, Eric, Whitelaw, William A, Remmers, John E
• Format: Journal Article
• Language: English
• Published: London BMJ Publishing Group Ltd and British Thoracic Society 01-04-2000; BMJ; BMJ Publishing Group LTD; BMJ Group
• Subjects: Adult; Aged; Aged, 80 and over; Algorithms; Angina pectoris; Automation; Biological and medical sciences; Cardiovascular disease; Coronary vessels; Female; Home environment; Humans; Investigative techniques, diagnostic techniques (general aspects); Laboratories; Male; Medical sciences; Middle Aged; Original; oximetry; Oximetry - methods; Oxygen saturation; Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques; polysomnography; Polysomnography - methods; portable monitor; Prospective Studies; Respiratory system; Sensitivity and Specificity; Sleep apnea; Sleep Apnea Syndromes - diagnosis; sleep apnoea; United States > US; Human; Oxymetry; Sleep apnea syndrome; Respiratory disease; Sleep disorder; Exploration; Polysomnography; Algorithm; Electrodiagnosis; Diagnosis; Technique; Automatic analysis; Comparative study
• DOI: 10.1136/thorax.55.4.302
• EISSN: 1468-3296
• ISSN: 0040-6376

BACKGROUND The gold standard diagnostic test for obstructive sleep apnoea (OSA) is overnight polysomnography (PSG) which is costly in terms of time and money. Consequently, a number of alternatives to PSG have been proposed. Oximetry is appealing because of its widespread availability and ease of application. The diagnostic performance of an automated analysis algorithm based on falls and recovery of digitally recorded oxygen saturation was compared with PSG. METHODS Two hundred and forty six patients with suspected OSA were randomly selected for PSG and automated off line analysis of the digitally recorded oximeter signal. RESULTS The PSG derived apnoea hypopnoea index (AHI) and oximeter derived respiratory disturbance index (RDI) were highly correlated (R = 0.97). The mean (2SD) of the differences between AHI and RDI was 2.18 (12.34)/h. The sensitivity and specificity of the algorithm depended on the AHI and RDI criteria selected for OSA case designation. Using case designation criteria of 15/h for AHI and RDI, the sensitivity and specificity were 98% and 88%, respectively. If the PSG derived AHI included EEG based arousals as part of the hypopnoea definition, the mean (2SD) of the differences between RDI and AHI was –0.12 (15.62)/h and the sensitivity and specificity profile did not change significantly. CONCLUSIONS In a population of patients suspected of having OSA, off line automated analysis of the oximetry signal provides a close estimate of AHI as well as excellent diagnostic sensitivity and specificity for OSA.